Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer.

Background: The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. Methods: This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. Results: MCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and ß-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. Conclusion: The combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Performance assessment of an artificial intelligence-based coronary artery calcium scoring algorithm in non-gated chest CT scans of different slice thickness.

Background: The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm). Methods: A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined. Results: A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively. Conclusions: The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.

Cushing's syndrome caused by giant Ewing's sarcoma of the kidney: A case report and review of literature.

BACKGROUND: Primary renal Ewing's sarcoma (ES) is extremely rare, and only two cases causing Cushing's syndrome (CS) have been reported to date. We report that the case of an 18-year-old patient is diagnosed primary renal ES with typical CS characterized by purple stripes, weight gain, and hypertension. CASE SUMMARY: CS was first diagnosed by laboratory testing. A huge tumor was revealed in the kidney following an imaging examination. Moreover, brain and bone metastases were observed. After comprehensive treatment, primarily based on surgery, primary renal ES was pathologically diagnosed with a typical EWSR1-FLI1 genetic mutation through genetic testing. Furthermore, the glucocorticoid level returned to normal. By the ninth postoperative month of follow-up, the patient was recovering well. Cushing-related symptoms had improved, and a satisfactory curative effect was achieved. CONCLUSION: Primary renal ES, a rare adult malignant tumor, can cause CS and a poor prognosis.

High-normal serum bilirubin is a useful indicator to assess the risk of diabetic retinopathy in type 2 diabetes: A real-world study.

Background: To investigate the association of serum bilirubin within normal range, especially unconjugated bilirubin (UCB), with diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods: In this cross-sectional, real-world study, 7617 T2DM patients were stratified into quartiles based on serum UCB levels. DR was determined by digital fundus photography and further classified into non-proliferative diabetic retinopathy (NPDR) and PDR. The associations of serum bilirubin levels and UCB quartiles with DR were investigated by logistic regression analysis. Results: After controlling for age, sex, and diabetes duration, the DR prevalence was significantly decreased across the serum UCB quartiles (40.4 %, 33.4 %, 29.7 %, 26.6 % for each quartile, respectively, p < 0.001 for trend). The subjects with DR had lower serum total bilirubin (TB) and UCB, rather than conjugated bilirubin (CB), compared with those without DR (p = 0.003 for TB, p < 0.001 for UCB, and p = 0.528 for CB, respectively), while all three types of serum bilirubin in the subjects with PDR were obviously lower than those with NPDR (p = 0.006 for TB, and p < 0.001 for UCB and CB, respectively). After adjustment for confounding factors, logistic regression demonstrated negative associations of serum TB and UCB levels, rather than CB, with the presence of DR (OR: 0.844, 95%CI: 0.774-0.920, p < 0.001 for TB; OR: 0.828, 95%CI: 0.763-0.899, p < 0.001 for UCB; and OR: 0.984, 95%CI: 0.900-1.074, p = 0.713 for CB, respectively). Additionally, a fully-adjusted analysis revealed a negative correlation between UCB quartiles and DR (p < 0.001). Conclusion: High-normal serum TB and UCB were closely associated with the decreased odds of DR, while all types of serum bilirubin were negatively correlated with the severity of DR in T2DM patients. Serum bilirubin may be used as a potential indicator to assess the risk and severity of DR in T2DM.

Refractory wastewater shapes bacterial assembly and key taxa during long-term acclimatization.

Bacterial assembly and key taxa during long-term acclimatization in refractory wastewater treatment systems is of paramount importance for optimizing system performance and improving management strategies. Therefore, this study employed high-throughput sequencing, coupled with machine learning models and statistical analysis approaches, to comprehensively elucidate key features of bacterial communities and assembly processes in pesticide wastewater treatment systems. A nine-month monitoring showed substantial variation in diversity and composition of bacterial community between two interconnected biological treatment units (designated as BA and PA). Dynamics of bacterial communities in both units were similar. Moreover, water quality played crucial roles in regulating the bacterial community structure of activated sludge, which were primarily driven by deterministic patterns. Homogeneous selection contributed to 62.85 % and 64.43 % of the variations in BA and PA samples, respectively. Additionally, network analysis revealed significant modularity in bacterial compositions in both groups. Linear regression analysis identified major bacterial modules associated with metabolism and degradation functions. Notably, Module 2 in PA samples has significant positive correlations with functions related to metabolism of nucleotide, amino acid, and xenobiotics, as well as benzoate degradation. Furthermore, key taxa in ecological modules identified by Random Forest model, such as Pseudomonas, Sphingobium, and PHOS-HE28, were dominant populations with metabolism and degradation functions. Particularly, Sphingobium, appeared to be a potential multifunctional degrading bacterium, related to amino acid and xenobiotics metabolism, as well as fatty acid, valine, leucine, isoleucine, fluorobenzoate, and aminobenzoate degradation. These findings are important for developing operating strategies to maintain stable system performance during refractory wastewater treatment.

Effect of inserting Cr in promoting the deep oxidation of dichloromethane over Co/WNb catalysts at low temperatures.

Enhancing catalytic activity while inhibiting the generation of chlorine byproducts is essential in the catalytic oxidation process of chlorinated volatile organic compounds (CVOCs). In this study, Cr-modified Co/WNb catalysts were synthesized and utilized for the degradation of dichloromethane (DCM). It was found that the moderate introduction of Cr exposed more Cr6+ on the catalyst surface due to the interaction between cobalt and chromium oxides, which promoted the generation of more chemisorbed oxygen on the surface, thus improving the redox properties and enhancing the activity of the catalysts. Additionally, the introduction of Cr increased the B acid sites of the catalysts, promoting the breaking of C-Cl bonds and the removal of dissociated Cl- Meanwhile, the improved redox properties also allowed further oxidation of the dissociated activated intermediate products and inhibited the generation of chlorine byproducts. The catalyst activity was optimal when the Cr to Co molar ratio was 4, which the T90 of DCM was 256 °C and the monochloromethane selectivity was only 1.7 %. Moreover, Co4Cr/WNb showed excellent chlorine and water resistance, making it an ideal candidate for CVOC degradation.

Evident structural anisotropies arising from near-zero particle asphericity in granular spherocylinder packings.

With magnetic resonance imaging experiments, we study packings of granular spherocylinders with merely 2% asphericity. Evident structural anisotropies across all length scales are identified. Most interestingly, the global nematic order decreases with increasing packing fraction, while the local contact anisotropy shows an opposing trend. We attribute this counterintuitive phenomenon to a competition between gravity-driven ordering aided by frictional contacts and a geometric frustration effect at the marginally jammed state. It is also surprising to notice that such slight particle asphericity can trigger non-negligible correlations between contact-level and mesoscale structures, manifested in drastically different nonaffine structural rearrangements upon compaction from that of granular spheres. These observations can help improve statistical mechanical models for the orientational order transformation of nonspherical granular particle packings, which involves complex interplays between particle shape, frictional contacts, and external force field.

Immobilization of cross-linked enzymes aggregates on hierarchical covalent organic frameworks: Highly stable chemoenzymatic nanoreactor for asymmetric synthesis of optically active halohydrins.

Organometallic catalyst is extensively applied for the non-enzymatic regeneration of nicotinamide adenine dinucleotide (phosphate) cofactors, but suffering from the mutual inactivation with the enzymes in one pot. The spatially separated immobilization of organometallic catalyst and enzymes on suitable carriers not only can reduce their mutual inhabitation but also can enhance their reusability. Here in this work, we present a hierarchical porous COFs (HP-TpBpy) that incorporated with [(Cp*RhCl2]2 to generate the metalized COF, Rh-HP-TpBpy. The obtained Rh-HP-TpBpy exhibited superior performance in nicotinamide adenine dinucleotide (NADH) and nicotinamide adenine dinucleotide phosphate (NADPH) regeneration using formate as the hydride donor, significantly outperforming the natural formate dehydrogenases in cofactor preference toward NADP+. Subsequently, the Lactobacillus fermentum short-chain dehydrogenase/reductase 1 (LfSDR1) was then cross-linked into enzyme aggregates (CLEA) and immobilized on hierarchical Rh-HP-TpBpy, achieving the integrated chemoenzymatic catalyst, LfSDR1@Rh-HP-TpBpy, which can catalyze the chemoenzymatic reduction of halogenated aryl ketones and give the corresponding optically active halohydrins with high conversion and enantiomeric excess (ee) value up to 99 %. The LfSDR1@Rh-HP-TpBpy also exhibits largely enhanced stability compared with the free LfSDR1 and the CLEAs-LfSDR1, enabling its excellent reusability.

Electron Deficiency is More Important than Conductivity in C-N Coupling for Electrocatalytic Urea Synthesis.

The electrocatalytic C-N coupling from CO2 and nitrate emerges as one of the solutions for waste upgrading and urea synthesis. In this work, we constructed electron-deficient Cu sites by the strong metal-polymer semiconductor interaction, to boost efficient and durable urea synthesis. In situ Raman spectroscopy identified the existence of electron-deficient Cu sites and was able to withstand electrochemical reduction conditions. Operando synchrotron-radiation Fourier transform infrared spectroscopy and theoretical calculations disclosed the vital role of electron-deficient Cu in adsorption and C-N coupling of oxygen-containing species. The electron-deficient Cu displayed a high urea yield rate of 255.0 mmol h-1 g-1 at -1.4 V versus the reversible hydrogen electrode and excellent electrochemical durability, superior than that of non-electron-deficient counterpart with conductive carbon material as the support. It can be concluded that the regulation of site electronic structure is more important than the optimization of catalyst conductive properties in the C-N coupling reactions.

MicroRNA-522-3p promotes brain metastasis in non-small cell lung cancer by targeting Tensin 1 and modulating blood-brain barrier permeability.

Brain metastases account for more than 50% of intracranial central nervous system tumors. The blood-brain barrier (BBB) is mainly composed of endothelial cells, which exhibit low endocytosis and high efflux pumps. Although they are connected by continuous tight junctions and serve as a protective insulation, the BBB does not prevent the development of brain metastases from non-small cell lung cancer (NSCLC). Improving understanding on the mechanisms underlying the development of brain metastasis and the differential molecular characteristics relative to the primary tumor are therefore key in the treatment of brain metastases. This study evaluated the differential expression of miR-522-3p in NSCLC and brain metastases using the Gene Expression Omnibus database. NSCLC brain metastasis model was constructed to screen for cell lines that demonstrated high potential for brain metastasis; We also observed differential expression of miRNA-522-3p in the paraffin-embedded specimens of non-small cell lung cancer and brain metastases from our hospital. The molecular biological functions of miRNA-522-3p were verified using 5-ethynyl-2'-deoxyuridine (EdU) proliferation assay and Transwell invasion assays. RNA-seq was employed to identify downstream target proteins, and the dual-luciferase reporter assay confirmed Tensin 1 (TNS1), a protein that links the actin cytoskeleton to the extracellular matrix, as the downstream regulatory target protein. In vitro blood-brain barrier models and co-culture models were constructed to further identify the role of miRNA-522-3p and TNS1; the expression of BBB-related proteins (ZO-1 and OLCN) was also identified. In vivo experiments were performed to verify the effects of miRNA-522-3p on the time and incidence of NSCLC brain metastasis. The results showed significantly high expression in GSE51666; consistent results were obtained in brain metastasis cells and paraffin samples. RNA-seq combined with miRNA target protein prediction demonstrated TNS1 to be directly downstream of miR-522-3p and to be associated with cell proliferation and invasion. By regulating ZO-1 and OCLN expression, mi-522-3p/TNS1 may increase tumor cell penetration through the BBB while decreasing its permeability. In vivo, miR-522-3p was further demonstrated to significantly promote the formation of brain metastases. miR-522-3p/TNS1 can affect BBB permeability and encourage the growth of brain metastases by modifying the BBB TJ proteins. This axis offers new therapeutic targets for the prevention of brain metastasis.

Effect of Hesperidin on Chronic Unpredictable Mild Stress-Related Depression in Rats through Gut-Brain Axis Pathway.

OBJECTIVES: To determine the pharmacological impact of hesperidin, the main component of Citri Reticulatae Pericarpium, on depressive behavior and elucidate the mechanism by which hesperidin treats depression, focusing on the gut-brain axis. METHODS: Fifty-four Sprague Dawley male rats were randomly allocated to 6 groups using a random number table, including control, model, hesperidin, probiotics, fluoxetine, and Citri Reticulatae Pericarpium groups. Except for the control group, rats in the remaining 5 groups were challenged with chronic unpredictable mild stress (CUMS) for 21 days and housed in single cages. The sucrose preference test (SPT), immobility time in the forced swim test (FST), and number in the open field test (OFT) were performed to measure the behavioral changes in the rats. Enzyme-linked immunosorbent assay was used to determine the levels of 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF) in brain tissue, and the histopathology was performed to evaluate the changes of colon tissue, together with sequencing of the V3-V4 regions of 16S rRNA gene on feces to explore the changes of intestinal flora in the rats. RESULTS: Compared to the control group, the rats in the model group showed notable reductions in body weight, SPF, and number in OFT (P<0.01). Hesperidin was found to ameliorate depression induced by CUMS, as seen by improvements in body weight, SPT, immobility time in FST, and number in OFT (P<0.05 or P<0.01). Regarding neurotransmitters, it was found that at a dose of 50 mg/kg hesperidin treatment upregulated the levels of 5-HT and BDNF in depressed rats (P<0.05). Compared to the control group, the colon tissue of the model group exhibited greater inflammatory cell infiltration, with markedly reduced numbers of goblet cells and crypts and were significantly improved following treatment with hesperidin. Simultaneously, the administration of hesperidin demonstrated a positive impact on the gut microbiome of rats treated with CUMS, such as Shannon index increased and Simpson index decreased (P<0.01), while the abundance of Pseudomonadota and Bacteroidota increased in the hesperidin-treated group (P<0.05). CONCLUSION: The mechanism responsible for the beneficial effects of hesperidin on depressive behavior in rats may be related to inhibition of the expressions of BDNF and 5-HT and preservation of the gut microbiota.

The value of metagenomic next-generation sequencing with different nucleic acid extracting methods of cell-free DNA or whole-cell DNA in the diagnosis of non-neutropenic pulmonary aspergillosis.

Purpose: Metagenomic next-generation sequencing(mNGS) is a novel molecular diagnostic technique. For nucleic acid extraction methods, both whole-cell DNA (wcDNA) and cell-free DNA (cfDNA) are widely applied with the sample of bronchoalveolar lavage fluid (BALF). We aim to evaluate the clinical value of mNGS with cfDNA and mNGS with wcDNA for the detection of BALF pathogens in non-neutropenic pulmonary aspergillosis. Methods: mNGS with BALF-cfDNA, BALF-wcDNA and conventional microbiological tests (CMTs) were performed in suspected non-neutropenic pulmonary aspergillosis. The diagnostic value of different assays for pulmonary aspergillosis was compared. Results: BALF-mNGS (cfDNA, wcDNA) outperformed CMTs in terms of microorganisms detection. Receiver operating characteristic (ROC) analysis indicated BALF-mNGS (cfDNA, wcDNA) was superior to culture and BALF-GM. Combination diagnosis of either positive for BALF-mNGS (cfDNA, wcDNA) or CMTs is more sensitive than CMTs alone in the diagnosis of pulmonary aspergillosis (BALF-cfDNA+CMTs/BALF-wcDNA+CMTs vs. CMTs: ROC analysis: 0.813 vs.0.66, P=0.0142/0.796 vs.0.66, P=0.0244; Sensitivity: 89.47% vs. 47.37%, P=0.008/84.21% vs. 47.37%, P=0.016). BALF-cfDNA showed a significantly greater reads per million (RPM) than BALF-wcDNA. The area under the ROC curve (AUC) for RPM of Aspergillus detected by BALF-cfDNA, used to predict "True positive" pulmonary aspergillosis patients, was 0.779, with a cut-off value greater than 4.5. Conclusion: We propose that the incorporation of BALF-mNGS (cfDNA, wcDNA) with CMTs improves diagnostic precision in the identification of non-neutropenic pulmonary aspergillosis when compared to CMTs alone. BALF-cfDNA outperforms BALF-wcDNA in clinical value.

Influenza A virus infection activates STAT3 to enhance SREBP2 expression, cholesterol biosynthesis, and virus replication.

Cellular cholesterol plays an important role in influenza A virus (IAV) endocytosis and replication. However, how IAV infection regulates cholesterol biosynthesis remains poorly understood. Here, we report that IAV infection activates SREBP2 and induces the expression of HMGCR, a rate-limiting enzyme in cholesterol synthesis pathway. SREBP2 deficiency suppresses IAV-induced HMGCR expression and virus replication. Mechanistically, IAV infection activates JAK2 and STAT3, inhibition of JAK2 and STAT3 activity by their inhibitors or by gene knockout downregulates IAV-induced SREBP2 and HMGCR expression and IAV replication, reduces the content of cellular cholesterol and virus binding to host cells. Exogenous cholesterol reverses the inhibitory effect of S3I-201 and STAT3 deficiency on virus replication. STAT3 or JAK2 overexpression increases the expression of SREBP2 and its downstream target genes, leading to increased IAV replication. These observations collectively suggest that STAT3 activation facilitates IAV replication by inducing SREBP2 expression and increasing cholesterol biosynthesis.

Scutellarein derivatives with histamine H3 receptor antagonism and cholinesterase inhibitory potency as multi target-directed ligands for possible Alzheimer's disease therapy.

A series of scutellarein 7-l-amino acid carbamate-4'-cycloalkylamine propyl ether conjugates were designed and synthesized for the first time as multifunctional agents for Alzheimer's disease (AD) therapy. The designed compounds exhibited more balanced and effective multi-target potency. Among them, compound 11l, l-Valine carbamate derivative of scutellarein cycloheptylamine ether, exhibited the most potent inhibition of electric eel AChE enzymes and human AChE enzymes, with an IC50 values of 7.04 µM and 9.73 µM, respectively. Moreover, 11l exhibited more potent H3R antagonistic activities than clobenpropit, with an IC50 value of 1.09 nM. Compound 11l not only displayed excellent inhibition of self- and Cu2+-induced Aß1-42 aggregation (95.48 % and 88.63 % inhibition, respectively) but also induced the disassembly of self- and Cu2+-induced Aß fibrils (80.16 % and 89.30 % disaggregation, respectively). Moreover, 11l significantly reduced tau protein hyperphosphorylation induced by Aß25-35. It exhibited effective antioxidant activity and neuroprotective potency, and inhibited RSL3-induced PC12 cell ferroptosis. Assays of hCMEC/D3 and hPepT1-MDCK cell line permeability indicated that 11l would have optimal blood-brain barrier permeability and intestinal absorption characteristics. In addition, in vivo studies revealed that compound 11l significantly attenuated learning and memory impairment in an AD mouse model. Finally, a pharmacokinetic characterization of 11l indicated favorable druggability and pharmacokinetic properties. Taken together, our results suggest that 11l is a potential candidate for AD treatment and merits further investigation.

A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis.

Objective: Recognizing emotions from electroencephalography (EEG) signals is a challenging task due to the complex, nonlinear, and nonstationary characteristics of brain activity. Traditional methods often fail to capture these subtle dynamics, while deep learning approaches lack explainability. In this research, we introduce a novel three-phase methodology integrating manifold embedding, multilevel heterogeneous recurrence analysis (MHRA), and ensemble learning to address these limitations in EEG-based emotion recognition. Approach: The proposed methodology was evaluated using the SJTU-SEED IV database. We first applied uniform manifold approximation and projection (UMAP) for manifold embedding of the 62-lead EEG signals into a lower-dimensional space. We then developed MHRA to characterize the complex recurrence dynamics of brain activity across multiple transition levels. Finally, we employed tree-based ensemble learning methods to classify four emotions (neutral, sad, fear, happy) based on the extracted MHRA features. Main results: Our approach achieved high performance, with an accuracy of 0.7885 and an AUC of 0.7552, outperforming existing methods on the same dataset. Additionally, our methodology provided the most consistent recognition performance across different emotions. Sensitivity analysis revealed specific MHRA metrics that were strongly associated with each emotion, offering valuable insights into the underlying neural dynamics. Significance: This study presents a novel framework for EEG-based emotion recognition that effectively captures the complex nonlinear and nonstationary dynamics of brain activity while maintaining explainability. The proposed methodology offers significant potential for advancing our understanding of emotional processing and developing more reliable emotion recognition systems with broad applications in healthcare and beyond.

Effect of metabolic syndrome on testosterone levels in patients with metastatic prostate cancer: a real-world retrospective study.

Background: Metabolic syndrome (MetS) has been shown to have a negative impact on prostate cancer (PCa). However, there is limited research on the effects of MetS on testosterone levels in metastatic prostate cancer (mPCa). Objective: This study aims to investigate the influence of MetS, its individual components, and composite metabolic score on the prognosis of mPCa patients, as well as the impact on testosterone levels. Additionally, it seeks to identify MetS-related risk factors that could impact the time of decline in testosterone levels among mPCa patients. Methods: A total of 212 patients with mPCa were included in the study. The study included 94 patients in the Non-MetS group and 118 patients in the combined MetS group. To analyze the relationship between MetS and testosterone levels in patients with mPCa. Additionally, the study aimed to identify independent risk factors that affect the time for testosterone levels decline through multifactor logistic regression analysis. Survival curves were plotted by the Kaplan-Meier method. Results: Compared to the Non-MetS group, the combined MetS group had a higher proportion of patients with high tumor burden, T stage ≥ 4, and Gleason score ≥ 8 points (P < 0.05). Patients in the combined MetS group also had higher lowest testosterone values and it took longer for their testosterone to reach the lowest level (P < 0.05). The median progression-free survival (PFS) time for patients in the Non-MetS group was 21 months, while for those in the combined MetS group it was 18 months (P = 0.001). Additionally, the median overall survival (OS) time for the Non-MetS group was 62 months, whereas for the combined MetS group it was 38 months (P < 0.001). The median PFS for patients with a composite metabolic score of 0-2 points was 21 months, 3 points was 18 months, and 4-5 points was 15 months (P = 0.002). The median OS was 62 months, 42 months, and 29 months respectively (P < 0.001). MetS was found to be an independent risk factor for testosterone levels falling to the lowest value for more than 6 months. The risk of testosterone levels falling to the lowest value for more than 6 months in patients with MetS was 2.157 times higher than that of patients with Non-MetS group (P = 0.031). Patients with hyperglycemia had a significantly higher lowest values of testosterone (P = 0.015). Additionally, patients with a BMI ≥ 25 kg/m2 exhibited lower initial testosterone levels (P = 0.007). Furthermore, patients with TG ≥ 1.7 mmol/L experienced a longer time for testosterone levels to drop to the nadir (P = 0.023). The lowest value of testosterone in the group with a composite metabolic score of 3 or 4-5 was higher than that in the 0-2 group, and the time required for testosterone levels to decrease to the lowest value was also longer (P < 0.05). Conclusion: When monitoring testosterone levels in mPCa patients, it is important to consider the impact of MetS and its components, and make timely adjustments to individualized treatment strategies.

Biomaterials with cancer cell-specific cytotoxicity: challenges and perspectives.

Significant advances have been made in materials for biomedical applications, including tissue engineering, bioimaging, cancer treatment, etc. In the past few decades, nanostructure-mediated therapeutic strategies have been developed to improve drug delivery, targeted therapy, and diagnosis, maximizing therapeutic effectiveness while reducing systemic toxicity and side effects by exploiting the complicated interactions between the materials and the cell and tissue microenvironments. This review briefly introduces the differences between the cells and tissues of tumour or normal cells. We summarize recent advances in tumour microenvironment-mediated therapeutic strategies using nanostructured materials. We then comprehensively discuss strategies for fabricating nanostructures with cancer cell-specific cytotoxicity by precisely controlling their composition, particle size, shape, structure, surface functionalization, and external energy stimulation. Finally, we present perspectives on the challenges and future opportunities of nanotechnology-based toxicity strategies in tumour therapy.

TaqTth-hpRNA: a novel compact RNA-targeting tool for specific silencing of pathogenic mRNA.

Pathogenic allele silencing is a promising treatment for genetic hereditary diseases. Here, we develop an RNA-cleaving tool, TaqTth-hpRNA, consisting of a small, chimeric TaqTth, and a hairpin RNA guiding probe. With a minimal flanking sequence-motif requirement, in vitro and in vivo studies show TaqTth-hpRNA cleaves RNA efficiently and specifically. In an Alzheimer's disease model, we demonstrate silencing of mutant APPswe mRNA without altering the wild-type APP mRNA. Notably, due to the compact size of TaqTth, we are able to combine with APOE2 overexpression in a single AAV vector, which results in stronger inhibition of pathologies.

The research landscape of ferroptosis in neurodegenerative disease: a bibliometric analysis.

Background: Ferroptosis, a newly proposed concept of programmed cell death, has garnered significant attention in research across different diseases in the last decade. Despite thorough citation analyses in neuroscience, there is a scarcity of information on ferroptosis research specifically related to neurodegenerative diseases. Method: The Web of Science Core Collection database retrieved relevant articles and reviews. Data on publications, countries, institutions, authors, journals, citations, and keywords in the included studies were systematically analyzed using Microsoft Excel 2019 and CiteSpace 6.2.R7 software. Result: A comprehensive analysis and visualization of 563 research papers on ferroptosis in neurodegenerative diseases from 2014 to 2023 revealed emerging research hotspots and trends. The number of annual publications in this field of study has displayed a pattern of stabilization in the early years of the decade, followed by a notable increase in the later years and peaking in 2023 with 196 publications. Regarding publication volume and total citations, notable research contributions were observed from countries, institutions, and authors in North America, Western Europe, and China. Current research endeavors primarily focus on understanding the intervention mechanisms of neurodegenerative diseases through the ferroptosis pathway and exploring and identifying potential therapeutic targets. Conclusion: The study highlights key areas of interest and emerging trends in ferroptosis research on neurodegenerative diseases, offering valuable insights for further exploration and potential directions for diagnosing and treating such conditions.